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Background: Smoking rates in the United States have been reduced in the past decades to 15% of the general population. However, up to 88% of people with psychiatric symptoms still smoke, leading to high rates of disease and mortality. Therefore, there is a great need to develop smoking cessation interventions that have adequate levels of usability and can reach this population. Objective: The objective of this study was to report the rationale, ideation, design, user research, and final specifications of a novel smoking cessation app for people with serious mental illness (SMI) that will be tested in a feasibility trial. Methods: We used a variety of user-centered design methods and materials to develop the tailored smoking cessation app. This included expert panel guidance, a set of design principles and theory-based smoking cessation content, development of personas and paper prototyping, usability testing of the app prototype, establishment of app’s core vision and design specification, and collaboration with a software development company. Results: We developed Learn to Quit, a smoking cessation app designed and tailored to individuals with SMI that incorporates the following: (1) evidence-based smoking cessation content from Acceptance and Commitment Therapy and US Clinical Practice Guidelines for smoking cessation aimed at providing skills for quitting while addressing mental health symptoms, (2) a set of behavioral principles to increase retention and comprehension of smoking cessation content, (3) a gamification component to encourage and sustain app engagement during a 14-day period, (4) an app structure and layout designed to minimize usability errors in people with SMI, and (5) a set of stories and visuals that communicate smoking cessation concepts and skills in simple terms. Conclusions: Despite its increasing importance, the design and development of mHealth technology is typically underreported, hampering scientific innovation. This report describes the systematic development of the first smoking cessation app tailored to people with SMI, a population with very high rates of nicotine addiction, and offers new design strategies to engage this population. mHealth developers in smoking cessation and related fields could benefit from a design strategy that capitalizes on the role visual engagement, storytelling, and the systematic application of behavior analytic principles to deliver evidence-based content. (JMIR Serious Games 2018;6(1):e2) doi: 10.2196/games.8881 KEYWORDS smoking cessation; mHealth; serious mental illness; user-centered design; gamification; acceptance and commitment therapy http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al cessation app designed for the general population. Specifically, Introduction we demonstrated that experiencing mental health symptoms, being female, and having low levels of educational attainment Background predicted low levels of engagement with the app at a 3-month Smoking rates in the United States have been reduced to 15% follow-up . In subsequent research, we further identified in the past decades . However, this downward trend is not specific usability barriers encountered by people with SMI when present in people with serious mental illness (SMI) . This using NCI QuitPal, a smoking cessation app developed by the population, which is characterized by people with chronic mental National Cancer Institute (NCI) . Despite adherence to US health symptoms and functional impairments that interfere with Clinical Practice Guidelines (USCPG) for smoking cessation, major life activities, typically encompasses individuals with a our study directly showed that NCI QuitPal led to critical diagnosis of schizophrenia, schizoaffective, bipolar, and performance errors and low user experience among smokers recurrent major depression . People with SMI have smoking with SMI, suggesting the need for new design approaches for rates of up to 88% [4-6], have high levels of disease , and this population. lose 25 years of life expectancy . Goal of This Study Survey research indicates that this population has rates of From these initial studies, we planned to develop Learn to Quit, adoption of mobile technology that range between 72% and a smoking cessation app tailored to this often neglected and 81% [9,10], closing the gap with the 95% adoption of the general vulnerable population. Consistent with the need to report the population , and thus presenting an opportunity to develop user-centered design process of mHealth interventions, the aim mobile smoking cessation apps that address the treatment needs of this paper was to describe the rationale, ideation, prototyping, of this population. design, user research, and final feature set of Learn to Quit, a Despite the increasing number of digital interventions developed smoking cessation app tailored to individuals with SMI that for people with SMI [12-17], no mobile intervention for smoking will be subsequently tested in a randomized controlled feasibility cessation has been described in the scientific literature for this trial (clinicaltrials.gov NCT03069482). vulnerable patient population. This shortage of smoking cessation mHealth research for SMI is not surprising when Methods considering the larger context of smoking cessation mHealth. There are over 540 smoking cessation apps in the market , Our user-centered design methods and materials are summarized but only 2 have been tested in randomized controlled trials in Figure 1. This formative study was organized in 7 phases [19,20], and to our knowledge, there are no reports of their consistent with the user-centered design framework [31,32], user-centered design research. which includes the following key activities: (1) understanding and specifying the context of use (phase I); (2) specifying the This lack of reports on the user-centered design process of user and organizational requirements, such as the active mHealth interventions for smoking cessation is problematic, ingredients of the behavior change intervention (phases II-III); because (1) the determination of the active therapeutic (3) produce design solutions (phase IV); and (4) evaluate the ingredients delivered by an app should be the result of a careful design (phase V). Our design process also involved users design process, (2) poorly designed software systems have an throughout the design and development process, addressed the impact on their ultimate efficacy and when not usable can be a whole user experience (both usability and user experience), and waste of resources [21,22], and (3) unreported design research incorporated multidisciplinary perspectives, which are key undermines design reproducibility and our body of knowledge. principles in user-centered design . Thus, user-centered design research of mHealth interventions is not only an important step to ensure their efficacy and Phases VI and VII are not part of the user-centered design usability, but also an important way to advance our scientific process per se, but are important steps in design implementation, knowledge. which are also documented in the user-centered design literature . Substantial progress in each phase was necessary to initiate People with SMI can have very low levels of adherence to digital meaningful progress in the following phase. However, at times, interventions [12,23], which calls for an user-centered design these phases overlapped with each other. For example, phase process that addresses a series of known usability barriers in IV overlapped with phase V, because feedback from usability this population, such as persistent and moderate-to-severe mental testing was used to modify or edit the original sketches and health symptoms , low levels of educational attainment paper prototype. Conversely, completion of phase VI was a , cognitive deficits [26,27], and poor fine motor skills . required step to initiate phases VI and VII. All study procedures were approved by the Institutional Review Board of the Prior Work University of Washington. In previous research, we found a direct link between key demographic factors and engagement with SmartQuit, a smoking http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al Figure 1. Methods and materials. people with SMI , and it is supported by the general Phase I: Expert Panel literature with regard to the importance of adjusting designs To better understand the needs of our target population, we systems to meet the cognitive model of the user [39-41]. sought to get the perspective of patients from this population Phase III: Incorporating Evidence-Based Smoking and their providers. Expert guidance has been used in prior work Cessation Content to inform app development efforts . We formed an expert panel composed of 2 social workers, 1 psychiatric case manager, Smoking cessation app content was selected from behavior 2 psychiatrists, and 2 smokers with SMI. After a project change interventions supported by the empirical literature (eg, introduction, we addressed the panel with 2 research questions: clinical trials) and from process research suggesting a theoretical (1) What are the biggest challenges for people with SMI to quit link between intervention components and the symptoms smoking? and (2) How could we design a highly engaging app typically experienced by our target population (see below for smokers with SMI? The first and second authors took notes “Evidence-Based Smoking Cessation Content” subheading in from comments and observations, discussed them to identify the Results section). This process ensured the theoretical agreements and focal points, and organized them for qualitative grounding of the app and its evidence-based foundation. review. No formal thematic analysis of these interviews was Phase IV: Ideation, Sketching, and Paper Prototyping conducted. We used several user-centered design tools to ideate a prototype Phase II: Selection of Design Principles of the app. This included (1) the creation of personas, a Before designing the app, we prespecified 2 sets of design technique aimed at increasing the designer’s emotional principles: (1) general learning principles based on applied understanding of the end user by creating a short narrative of behavior analysis and (2) design principles specific to their motivations, context, and personal characteristics [42-44], individuals with SMI. Applied behavior analysis is a scientific and (2) sketching and paper prototyping, an important discipline focused on developing strategies and behavior component of the design process consisting the use of paper modification techniques in areas of social relevance based on drawings to quickly iterate on variations of app structure, app principles of learning. Use of these principles has shown promise interactions, and the layout of content [42,45]. for the treatment of addiction in people with SMI [35,36], and Phase V: Usability Testing of Paper Prototype these principles have been used in the design of games and other health apps . Design principles specific to individuals with Procedures SMI have typically addressed the cognitive deficits and mental Sketches and images developed during the ideation and paper health symptoms encountered by this population. This approach prototyping phase provided the basis for usability testing. To has been used in previous work designing technologies for simulate the app experience, we used app prototyping software http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al (POP, Marvel, London, UK). The software was installed on of user comments and behavior. Finally, for the purposes of this iPod Touch devices, and the prototype was presented to smokers study, we did not conduct observational coding of users’ with SMI during a single 45 min session. Our key inclusion behavior during task completion. Instead, we relied on the criteria were as follows: (1) being an adult who smokes at least qualitative analysis of user feedback and the SUS. 5 cigarettes per day, (2) receiving outpatient mental health Measures and Analysis treatment and medication by a psychiatric provider, and (3) The SUS is a valid and reliable 10-item 5-point Likert scale being fluent in with scores that range from 0 to 100. Higher scores indicate Usability testing procedures included (1) completing a series higher levels of usability, with scores above 68 indicating of tasks with the simulated app, (2) evaluation of user experience above-average usability . The SUS can be further analyzed with semistructured interviews, and (3) rating the prototype based on 2 subscales that measure usability and learnability of using the system usability scale (SUS) . Preceding our user the software system. Learnability refers to the user’s level of interviews with a series of hands-on tasks provided the user ease in gaining proficiency with a software system. These with a more in-depth experience with the app prototype, and subscales are interpreted following the same range and direction therefore allowed us to gather more meaningful and concrete of the overall scale . Semistructured interviews were feedback from users. Finally, our global assessment of usability, recorded, transcribed, and analyzed using thematic analysis, an the SUS, was conducted at the very end to give users an inductive qualitative method that organizes verbal content based opportunity to summarize their feedback. Given the iterative on similarity, dependence, and proximity to identify key themes nature of this testing phase, we modified app design features and opportunities for innovation [49-51]. after each participant and provided the new version to the Phase VI: Defining of the App’s Core Vision and following participant. This iterative process is standard in formative evaluations . Design Specifications We created a design specifications document that laid out the Our usability testing tasks evaluated the following elements of app’s overall vision, look and feel of the interface, and its basic the app prototype: (1) an introductory tutorial, (2) overall Home components and features . This document was used to Screen navigation (Figure 2, panel a), (3) overall Play Screen facilitate communication with a software development company. navigation (Figure 3, panel a), (4) access to technical coach feature (Figure 2, panel a), and (5) access to learning score and Phase VII: Work With a Software Development practice scores (Figure 2, panel a). Consistent with usability Vendor testing guidelines , our procedures reminded the interviewer In this final phase, we worked with a software vendor to to keep neutrality in response to user comments and behavior, materialize the vision and design specifications that resulted read the script verbatim to each user, and observe and keep track from our formative study. Figure 2. These wireframes represent how our app’s initial Home Screen evolved throughout our design process. From left to right: (a) Home Screen sketch, (b) pre-14 Home Screen, and (c) post-14 Home Screen. Wireframes (b) and (c) are examples of the 2 types of Home Screen status: dark green, indicating that the user is still completing the 14 modules of Learn to Quit, and light blue, indicating that the user already completed the Learn to Quit lessons and is ready for his first quit attempt. http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al Figure 3. These wireframes represent how our app’s initial Play Screen evolved throughout our design process. From left to right: (a) Play Screen sketch, (b) pre-14 journey map, and (c) post-14 journey map. Wireframe (a) presents a character that needs to “jump” from stone to stone to “pick up” skills for quitting while navigating through a “swamp of urges.” In wireframe (b), the user has completed the “Finding Your North Star” lesson and practiced the “Your North Star for Quitting” skills module. Wireframe (c) presents a user who has completed all levels of the Learn to Quit journey and motivated by his values for quitting has metaphorically reached “Learn to Quit Land”. that emphasizes setting up a quit date and monitoring Results maintenance. In this section, we describe the results of our formative study To solve this lack of motivation and concerns about the process by focusing on a summary of our expert panel feedback, of quitting, the panel outlined a series of strategies that could describing an initial paper prototype of the app and the results engage the users with this app. One of them was the use of of its usability testing. This formative study resulted in a clearer meaningful images and storytelling. Some level of gamification definition of the app’s core vision and helped define the scope was viewed as important to engage users with SMI. In addition, of work to be conducted by a software development company. the panel argued that as a way to compensate for the Results from the remaining user-centered design processes and overwhelming task of quitting smoking, the system should materials (ie, design principles, theory-based content, paper include progressive disclosures, make sure it rewards small prototyping) are presented throughout a final section that lays victories, and add external motivation (eg, money saved). Social out the app’s final characteristics and features and how they networking and monitoring of medication aids were also were informed by those processes and materials. mentioned. Finally, a few other themes emerged during our discussion, including challenges related to the use of technology Results From Our Formative Study in this population, the social context of these patients (eg, risk Expert Panel Results of having their smartphone being stolen), and the benefits of personalization and provider support during the initial stages The panel emphasized the specific challenges faced by this of nicotine withdrawal and beyond. population when trying to quit (Table 1). This included the panel members’ view that, as opposed to the general population, Initial Results From Ideation, Sketching, and App patients with SMI are more concerned about ongoing medical Prototyping and mental health challenges (eg, metabolic syndrome, On the basis of input from our expert panel and the authors’ suicidality) rather than dying from cancer. Although not experience as clinical providers, we created 3 personas: (1) consistent with the behavioral economics literature , in the Esteban, a 55-year-old male with schizophrenia, 35 years of panel members’ experience as providers, motivations to quit in smoking history, psychiatrically stable, and with a range of people with SMI are either different or less pronounced than in medical complications; (2) Martin, a 31-year-old male with a the general population. To add to this challenge, providers diagnosis of bipolar disorder, who is a light smoker, and holds indicated that both withdrawal symptoms and ongoing mental a stable job; and (3) Julia, a 43-year-old female with recurrent health symptoms were a common concern in their patients when major depression and a history of drug use, who is taking care discussing the possibility of quitting smoking (eg, triggering a of a young daughter. These personas guided the design process psychotic event). Needing preparation to quit was also and helped ensure that the initial prototype was responsive to emphasized, arguing that patients would prefer a system that the physical and emotional contexts of a broad spectrum of teaches them a set of skills for quitting, rather than a system http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al people with SMI, from psychotic to chronic affective disorders Screen (see Figure 4, panel a); a Quiz Screen (see Figure 5, (see Multimedia Appendix 1). panel a); a proof-of-concept smoking cessation module (see Figure 6, panel a); and an onboarding tutorial. To minimize These personas were used as inspiration to sketch our first cognitive demand in people with SMI, the app had only a few wireframes (ie, sets of images displaying the functional elements buttons (eg, “back,” “next,” and “Home”). These buttons were of a website or app) and the overall app prototype, which was large, an important feature given the fine motor deficits observed limited to a few basic components: A Home Screen (see Figure in people with SMI  and findings from previous research 2, panel a); a Play Screen (see Figure 3, panel a); a Tracking in this population [30,38]. Table 1. Expert panel themes. Each of the insights of our expert panel are organized by a question and accompanied by a short description. Questions and themes Description Question 1: What are the biggest challenges for people with serious mental illness to quit smoking? Motivation to quit Life expectancy is not generally a motivation to quit in this population Quitting without preparation Early attempts to quit without enough preparation, and/or lacking a step-down quitting process Withdrawal symptoms Fear of experiencing withdrawal symptoms days after quitting Mental health symptoms Ongoing anxiety, depression, stress, and psychotic symptoms during the quitting process Question 2: How could we design a highly engaging app for smokers with serious mental illness? Meaningful visuals The ability to display pictures of inspiring objects, people, sites, or pets Having a “video game” feel The appeal of video games or “game like” features (eg, “bingo”) Social networking The possibility of sharing with peers Storytelling The use of interactive characters (eg, dog) for storytelling Encouraging activation The importance of increasing activation (eg, exercise) to facilitate quitting and cope with withdrawal Progressive disclosure Unlocking bits and pieces of the app, such as a picture or a message, as the user makes progress Rewarding small victories Reinforcing small victories toward quitting (eg, one day without smoking) Money savings motivation Small money savings (eg, a few extra dollars a week) Medication aids Medication education and a system to help patients adhere to medication intake Miscellaneous themes Technological literacy Lack of smartphone knowledge was viewed as a potential barrier to app use Predominant use of Android The need to build an app in Android was viewed as important to secure access in this population Provider check-ins Flexible check-ins (eg, text messages) were emphasized to enhance engagement with the app Need for personalization An app that was customizable to each patient was deemed as important Stealing Concern that some patients with serious mental illness might have their devices stolen http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al Figure 4. These wireframes represent how our initial tracking feature evolved. From left to right: (a) tracking feature sketch, (b) cigarette tracking, and (c) personalized cigarette use feedback. As opposed to wireframe (a), in which we planned to use a single wireframe to collect all desirable tracking dimensions, in the final app, we used separate wireframes for each dimension (eg, smoking, mood). Note in (b) that users could report smoking half cigarette. Wireframe (c) is an example of personalized feedback following a user who reported smoking between 5 and 10 cigarettes. Figure 5. Wireframe examples of an initial sketch of a “Review Quiz” and a final Learning Module Quiz. Quizzes were presented at the end of the learning modules, and contained 3 questions each. From left to right: (a) Review Quiz sketch, (b) example of question for the “Key to Quitting” module, (c) feedback to correct answer that is followed by game reward sound, and (d) summary of quiz results, which indicates number of correct answers, best answer of all times, and number of practice stars gained (1 for each practice with a total of 3 per module). http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al Figure 6. Selection of wireframes of a smoking cessation skill (ie, Use Your Five Senses) designed to encourage self-awareness of our 5 senses. From top to bottom: (a) sketch of Use Your Five Senses skill and (b) final Use Your Five Senses skill module. Wireframes in panel (b) include five 10-second timers to assist the user focus their attention. They provide visual and tactile cues to mark the end of each practice of focused attention. Table 2. Key baseline features of usability testing subjects and corresponding system usability scale (SUS) scores. Scores above the usability standard cut-off (>68) are indicated in italics. Participant Mental health treatment Years in mental Years smoking Cigarettes per SUS usability SUS learnability SUS total number health (mean=20) day (mean= 80) (mean=60) (mean= 74) (mean=25) (mean=11) P1 Case manager, psychiatric nurse 25 9 13 72 100 78 P2 Case manager, psychiatric nurse 29 10 7 88 75 85 P3 Case manager, psychiatrist 20 32 10 44 25 40 P4 Case manager, psychiatrist 39 35 15 81 50 75 P5 Case manager, psychiatrist 12 14 10 94 88 93 System Usability Scale Usability Testing Results Overall, participants’ levels of usability with the prototype were A total of 5 daily smokers recruited from an outpatient mental above the standard cutoff, suggesting that the initial prototype health clinic participated in usability testing of the app prototype. had promise. This was reflected in both the overall scale and They averaged 44 years of age (standard deviation [SD] 7.5), the usability subscale. Conversely, the learnability subscale of and the majority were female (4/5) and had less than a college the SUS did not reach the standard cutoff, although it reached education (4/5). Of the 5 participants, 1 was multiracial, 1 high levels for 3 out of 5 individuals (see Table 2). African American, and the rest were white. Our sample group Thematic Analysis smoked an average of 11 cigarettes per day (SD 3), and most had an extended smoking history (mean 20 years, SD 13). Table 2 summarizes the results of the thematic analysis. To Although we did not conduct diagnostic interviews, all facilitate comparison with user testing of a nontailored app for participants were patients from a community mental health smoking cessation in the same population (NCI QuitPal), we clinic, had an assigned psychiatric case manager and a matched some of the themes resulting from our analysis with a psychiatric provider, were currently taking psychiatric previous usability testing study we conducted in smokers with medication, and on average had received mental health treatment SMI . for 25 years (SD 10). See Table 2 for a breakdown of individual baseline characteristics. http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al Key Learnings From Usability Testing of the Learn to Quit simplicity of the design and the use of cartoons and gamification, Prototype and had a positive response to our paper prototype of a smoking cessation skill (see Tables 3 and 4). Key learnings from usability testing can be organized in 3 areas: support for app design features, critical usability errors, and Second, usability testing identified a critical usability error with minor usability errors. First, we found that the following app our paper prototype. Specifically, it revealed that our original design features improved the apps usability: (1) the prototype’s Home Screen was confusing to most users (see Figure 2, panel reduced number of app layers, (2) removing the need to use a a). Users had difficulty in understanding the Home Screen keypad to enter and save information in the app, and (3) overall structure and the purpose of each Home Screen subpanel. This prototype’s simplicity. Although Learn to Quit’s simple app is reflected in the prototype’s suboptimal SUS learnability score. structure and navigation features were the result of a previous Finally, we identified minor usability problems with the user-centered design study we conducted in the same population prototype, including small font in the subpanels and confusion , this study allowed us to test a specific and concrete solution about specific language. These usability errors led to a final to those previously identified usability issues. User experience Home Screen that had a simpler layout and removed most of more directly confirmed that participants appreciated the its original displays and content (see Figure 2, panels b and c). Table 3. Usability testing results for Learn to Quit prototype (n=5) matched with comparable usability testing results from a previous user-centered design study (n=5) we conducted in a smoking cessation app designed for the general population (QuitPal) . Theme Quote/Observation/Feature Smoking cessation app (QuitPal); (SUS =65.5) Learn to Quit paper prototype; (SUS= 74) Difficulty entering information in the app Unable to “pull up the keypad” The need to use the keypad was removed from the prototype Difficulty saving information Failure to identify and press “save” button at the The need to use a “save” button was removed from top of screen the prototype Getting lost in app layers “It took me a long time to get back to that menu No observed confusion about how to return to the frame” Home Screen Tremor and fine motor skills “[buttons were] too close together” P5 : “I like how the letters are big” SUS: System Usability Scale; scores above the usability standard cut-off (>68) are indicated in italics. P: Participant. http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al Table 4. Themes identified during usability testing of the Learn to Quit app prototype (n=5). Themes Representative quote Interested in gamification of smoking cessation skills P1: “That would be so cool! A point every day” P2: “[the most exciting] the points” P4: “this would be kind of fun”; “So this is like a little game and people often play a lot of games” P5: “That’s a good thing […] that builds confidence” Drawn by cartoons and storytelling P3: “The cartoons, the whole thing. It’s got great spirit” P4: “that’s very cute!”; “I like it, it’s cartoony-like”; “it is very eye-catching” P5: “So you got a cartoon character! That’s what I was thinking. It was right on. That works for me”; “Mm, cartoon characters, yes!” Appreciating simplicity P2: “It was simple, informative, easy to use” P4: “[I like it]...when you’re a kid and you’re learning something new, it’s basic and it’s not all overstimulated, to put it that way…” P5: “It just goes right in your mind”; “this looks really simple too and it looks good” Proof of concept: Acceptance and Commitment P1: “I wish you guys could send it to me so that I could practice it and learn it” Therapy module showed promise P4: ”it’s like this too shall pass […], makes sense.”; “Mind and action skills. That’s pretty good. Because it’s like when you go to an AA” P5: “It relaxed me” Home Screen confusion P1: “It’s very small and I can’t see what it is.” P2: “I don’t know if that’s an “I” or not.” P3: “The first part of the app is so busy…” P: Participant. components have shown to predict smoking cessation outcomes Design Specifications and the App’s Core Vision [59-61]; (3) it is an intervention originally developed to provides On the basis of this formative study, we created a technical skills to cope with mental health symptoms ; and (4) it has document laying out the app’s structure and its core screens and been successfully adapted to people with SMI [62-65]. These features. We named the app Learn to Quit and synthesized the 4 characteristics made ACT a suitable evidence-based approach app’s core vision with the following: learn, practice, and play. for smoking cessation in this specific population. Learning referred to the process of being exposed to daily ACT has 3 components relevant to smoking cessation: modules that explain different smoking cessation concepts. awareness, or the smoker’s ability to recognize smoking triggers Brief quizzes would help the user retain and learn those and urges; openness, or the smoker’s willingness to experience materials. Practice referred to the actual practice of smoking smoking urges and triggers; and values activation, or the cessation skills in the form of brief daily exercises. Practice smoker’s active engagement in values-based activities related should lead to “mastery” of the learned materials. Play referred to health. The app content was therefore designed to deliver to the user’s opportunity to participate in a game comprising techniques related to each of those components, which predicted completing learning and practice modules and earning rewards smoking cessation outcomes in previous research [59,60]. A along the way. The role of play was to promote higher levels secondary active ingredient of our intervention was adherence of app engagement and commitment to learn. to key elements of USCPG for smoking cessation . These Software Development Timeline guidelines include setting up a quit date, proper use of medication aids (eg, nicotine patch), and preparing for relapse. In May 2015, we filed a Report of Innovation at the University These 2 active ingredients (ACT and USCPG) were integrated of Washington (ROI#47274) with the design specifications to ensure an evidence-based design approach to app document of the app and proceeded to approach a company to development. develop a software-coded version of the app. Learn to Quit was built between August 2015 and October 2015. Smashing Ideas We adapted ACT to a mobile format by creating 14 modules Inc. , a design and development agency, contributed to the of ACT+USCPG content and 14 modules of exercises to practice refining and enhancement of the app prototype. smoking cessation skills (see Multimedia Appendix 2). We chose a 14-day program that would be consistent with USCPG, Learn to Quit’s Core Features which recommends 2 weeks of preparation for quitting, and Evidence-Based Smoking Cessation Content would balance the need for gradual exposure to smoking cessation content while providing a concrete timeline. Each Learn to Quit’s main active ingredient is Acceptance and module presented a short narrative that exemplified Commitment Therapy (ACT) . We chose this behavior ACT+USCPG content and explained it from the perspective of change approach because (1) it has promising results across someone with nicotine addiction and SMI. Each concept was multiple smoking cessation clinical trials [19,55-58]; (2) its http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al presented in a variety of narratives and exercises to increase quitting skills along the way (see Figure 3). Gamification learning generalizability (see principle of multiple exemplar included interactive quizzes that provide immediate feedback training in Multimedia Appendix 3). In addition, each lesson about quiz results (see Figure 6, panel d), a component that has ended with a quiz designed to further enhance comprehension been correlated with long-term improvements in eHealth and retention (see Figure 5, panels b and c). We arranged these interventions . Feedback included auditory cues for right modules so that completing a lesson per day was necessary to or wrong answers and a reward system consisting of unlock new lessons the next day (see principle of differential checkmarks, badges, cups, and crowns (see Applied Behavior reinforcement of successive approximations in Multimedia Analysis section below). Repeated practice was encouraged Appendix 3). with a reward system that provided stars every time a module was completed. This star system was consistent with the Completion of skills modules was optional to increase the user’s narrative of the app game (ie, module “Finding Your North perceived behavioral control . ACT+USCPG content was Star”; see Multimedia Appendix 2). Sliding through module developed and organized so that it would gradually increase in screens was also followed by video game sounds and feedback. complexity as the user advances through each of the 3 levels We also added interactive elements to our tracking feature. (see section below on app gamification). Specifically, a 3-tiered system of personalized feedback was incorporated to diminish assessment burden. That is, we Simple Screens, Large Buttons, and a Predictable App categorized all possible answers to a specific question (“How Structure many cigarettes you smoked today?”) into 3 different levels To address the cognitive deficits observed in this population, and we created a custom message for each, acknowledging the we incorporated feedback from our formative study and users’ answer and encouragement to move forward in their followed recommendations from previous literature journey (eg, smoking less than 5 cigarettes; see Figure 4, panels [30,38-41,68]. See Multimedia Appendix 3 for a list of key b and c). design principles we used to address SMI in our app. The result was an app with simple screens, large buttons, and a simple Application of Principles of Applied Behavior Analysis structure. The Home Screen (Figure 2, panels b and c) only Behavioral principles (see Multimedia Appendix 3) informed included 2 buttons, 1 to access the Play Screen, which unlocked the organization and delivery of smoking cessation content. the module content described above, and 1 button to access the These principles had the goals of (1) increasing retention, app settings. Usability testing suggested that the Home Screen comprehension, and mastery of app content and (2) minimizing should incorporate only a few elements. Thus, the Home Screen the impact of the cognitive deficits and low educational had as its focus a character surrounded by a circle of attainment of our target population [26,27,71]. For example, checkmarks, indicating overall progress in modules’ completion. instead of providing the user with all smoking cessation content at once, we used the principle of successive approximations The Play Screen (Figure 3, panels b and c) represents a game  to lay out increasingly complex ACT content throughout in which each day the user takes a step forward toward a 14-day period (see Multimedia Appendix 2 for a list of all the completing a smoking cessation module. This screen had a lessons and skills). Then, we applied the principle of multiple linear structure, with older modules at the bottom and newer exemplar training . Consistent with this principle, we modules toward the top. Each module ranged between 6 and presented multiple examples of a given concept or skill 24 screens, all of which had a reduced amount of semantic throughout multiple modules as a means to foster skills information, a simple color palette, consistent font size and type, generalization to real-world settings. and a predictable structure. All remaining wireframes in the app were presented in the form of cartoon scripts divided in 2 panels: In addition, the app used a combination of antecedent and the upper panel included the text and the lower panel a consequential control strategies [72,74]. On the one hand, complementary image. We made each panel identical in size to antecedent control was used in the form of notifications and maximize consistency and minimize cognitive load. This format Home Screen messages to prompt the individual to complete was consistent with our panel’s emphasis on the use of certain modules or tasks (Figure 2, panel b: “New Lesson storytelling and interactive characters to engage patients with unlocked! Tap button to Start”). On the other hand, SMI. We also avoided the use of videos and audio. We consequential control was used by applying positive hypothesized that the use of sliding cartoons and vignettes could reinforcement arranged on a fixed ratio schedule . More maximize retention and comprehension of smoking cessation specifically, obtaining a new star was made contingent upon skills, as this format provides the user with more control over completing a module. This reinforcement ratio was used to the speed of presentation of smoking cessation content and the encourage users to complete those modules at least 3 times (ie, ability to stop or review a single vignette for as long as needed. a maximum of 3 stars could be obtained per module; see Figure 3, panels b and c), which could lead to a total of 84 stars (see Gamification of Smoking Cessation Content Figure 2, panels b and c). Additional modules could be Consistent with our expert panel’s feedback, usability testing, completed at any time but it was not incentivized with more and the empirical literature on the use of games not for stars. entertainment, such as health or education (ie, serious games) [67,69], we designed an app that gamifies smoking cessation A rewards scheme was also implemented to increase the content. The main concept was designing a game in which the reinforcing effect of responding correctly to lesson quizzes (see smoker metaphorically overcomes a swamp of urges and learns Figure 5, panel d). For example, an individual who gave 3 http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 11 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al correct answers to 3 questions would receive a “crown.” Badges user to call a preassigned technical coach. Eventually, this and cups were provided to users who responded 2 and 1 correct technical coaching role could be performed by addiction answers, respectively. Checkmarks were offered to users who counselors or case managers and therefore serve to integrate completed the quiz but did not have any correct answer. Finally, the app within ongoing health care at a community mental health we used negative reinforcement to promote daily completion clinic or larger health care organization. of app modules. Specifically, the number of days in a row in which the user completed a module was indicated with a big Discussion bold number at the bottom of the Home Screen (see Figure 2, Principal Findings panel b). Not completing a new module one day was penalized with starting the count again from zero. This paper reports the rationale, ideation, design, user research, and final features of a novel smoking cessation app developed Emphasis in Visual Engagement and Storytelling for people with SMI, a population in great need of novel Because our expert panel and usability testing strongly supported smoking cessation treatment. Building this app involved a the use of simple cartoons and visual storytelling, we created a user-centered design process that carefully considered a series gender-neutral character that rotated across the different stories of design principles to maximize comprehension and retention and metaphors presented in each module (see Figures 2,3, and of smoking cessation concepts, minimize the impact of known 6). The character enacts a variety of scenarios with the aim of challenges in people with SMI, and ensure the effective delivery exemplifying the experience of nicotine addiction and the of evidence-based smoking cessation content. smoking cessation skills offered in the app. Combination of Results from our user-centered design process informed the imagery and text has been recommended in previous literature features included in the final app. First, informed by our on mHealth in SMI populations . The stories were told in formative study and as suggested by the literature , we very short sentences designed to avoid cognitive overload and gradually delivered evidence-based smoking cessation content maximize module completion. The purpose of these images and using imagery and simple semantic content. The primary active stories is to evoke an emotional connection and increase ingredient of this evidence-based content was ACT, which is retention and comprehension of app content. For example, the an intervention that has empirical support as a smoking cessation Home Screen had 2 types of background: (1) dark green (Figure intervention [55-58] and as an intervention to treat individuals 2, panel b), to represent that the user is still completing the Learn with SMI [62-65]. This intervention was further integrated with to Quit journey and thus navigating through a “swamp of urges,” smoking cessation recommendations from USCPG  to and (2) light blue (Figure 2, panel c), to indicate that the user ensure alignment with best clinical practices. has reached “Learn to Quit land” and thus is ready to quit. Similarly, the background of each module used a consistent and Second, as suggested by the literature [30,38-41,68] and our muted color scheme that maps into the 3 dimensions of the app’s user testing of a paper prototype, we created an app with very theory-based content: (1) gray for Learning Modules, (2) blue simple screens, buttons, and a predictable app structure that led for Mind Skills, and (3) red for Action Skills. The function of to promising usability and user experience results. The paper this color scheme was to offer a simple and predictable layout prototype we created used a minimal set of app buttons and a that would be more likely to fit the cognitive model of the user very simple app structure, which probably contributed to scores [40,41]. Previous research in people with SMI also indicated above the usability standard cut-off. Our thematic analysis of that bright colors can be overwhelming in this population, thus user experience interviews was in line with these usability scores supporting our use of muted colors and a simplified color and further reinforced the inclusion of our final set of app’s core scheme . features. Access to Technical Coaching Third, input from a panel of experts in SMI led to the idea of Our expert panel indicated that “technological illiteracy” was incorporating app gamification, visual engagement, and common in patients with SMI (Table 1). In the panel members’ storytelling [67,69,75]. Use of these elements is consistent with opinion, many of their patients have never used a smartphone the serious games literature for smoking cessation published in device, and therefore using successfully an app for smoking this journal . More specifically, the app offered a module cessation could be a challenge. Furthermore, they commented to help users identify their core values for quitting and used on the complex social context of these patients and the potential these values as the game’s objective (ie, module “Your North need for more intense personal support. In our previous study Star for Quitting”), which increases the user’s perceived testing a smoking cessation app in a small sample of this behavioral control and intrinsic motivation . In addition, it population , we observed some of these challenges, but we offered a clearly structured game that had functional utility , also noted that there was a wide range of technological literacy that is, successfully completing the game involved being in this population, with some patients owning their own device exposed to a series of content known to help people quit and comfortably using smartphone apps. Therefore, we smoking. incorporated a simple technical coaching feature in our design Fourth, we implemented a number of applied behavior analysis that would serve as an aid to those that needed guidance, principles to maximize retention and comprehension of app whereas at the same time maintaining the core vision of the app content [37,76]. This included the use of progressive disclosures as a stand-alone intervention. This technical coaching feature of smoking cessation content, variation and repetition of that consisted of a button in the settings section that allowed the content, a reward system, and modules that gradually portrayed http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 12 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al complex smoking cessation concepts (eg, psychological Methods section, we emphasized this key activity by awareness). Application of these principles provided guidance incorporating phases II and III and by making an effort to ensure to adjust our design to the needs of our population and provided that our final design integrated concerns brought up by each of a level of conceptual clarity that linked our work with the these phases. behavior change literature at large. For example, although the Finally, a relevant aspect of this app is that it could be a good term “notification” is common in mHealth, this feature is example of the concept of universal design , for which essentially an antecedent control strategy, which has been systems tailored to specific groups of individuals with certain extensively used in the behavior change literature in areas such disabilities or challenges become inherently usable to larger as autism [77-79], individuals with dementia and cognitive groups of the population without those challenges. For example, impairments [80-82], and in cases of traumatic brain injury and even though the app addresses core usability barriers poor executive functioning [83,84]. experienced in people with SMI, in doing so, it explains in Finally, our development effort took into account simple terms very complex psychological concepts which might implementation considerations: (1) it used an Android operating engage smokers of young age, low literacy groups, and the system, which according to our panel was a common platform general public. Furthermore, the app’s systematic use of among people with SMI and tends to dominate the market behavior analytic principles to increase comprehension and among people with lower socioeconomic status (eg, individuals retention of ACT and USCPG is broadly applicable. Therefore, with disabilities) [85,86], and (2) it addressed the potential need Learn to Quit’s design approach could be generalizable to related for more intense personal support by including a technical coach mental health conditions, health behaviors, or the general public. feature within the app. In the future, this coaching role could Limitations be performed by addiction counselors or case managers in This study had several limitations. First, the number of patients community mental health clinics and address not just technical with SMI in the expert panel group (n=2) and the usability issues but also support to use of ACT skills to deal with smoking testing study (n=5) could have been small, leaving to question cravings or adherence to USCPG. whether a larger sample of people with SMI could have led to Comparison With Prior Work more feedback and opportunities for innovation. There is debate The study reported in this paper is consistent with user-centered among user-centered design researchers about the most design research of mobile apps for depression , smoking cost-efficient number of subjects to identify usability errors cessation , and work with diverse populations . [101,102], with the most traditional approach suggesting a Furthermore, Learn to Quit’s emphasis in gamification is sample size of 5 . Recruiting individuals with SMI is consistent with an app developed for depression, SuperBetter particularly challenging; therefore, we believe our sample size . was justified. To date, many apps have focused on the use of sensors and Second, our methods could have been more rigorous in several algorithms to track user context and provide personalized aspects. Specifically, results from our expert panel were not feedback [90-94]. Learn to Quit differs from this approach in transcribed and analyzed using a complete set of qualitative the sense that it is based on a more traditional form of methods, which could have led to the identification of additional engagement, visual storytelling. Visual storytelling is a core themes. However, rather than a thorough and comprehensive form of engagement common across human cultures . analysis of provider input, the goal of this expert panel was to Storytelling and visual engagement are implicated in emotional quickly gather initial insights and impressions that would orient arousal  and in attaching value and significance to sensory our imminent design process. Additionally, usability testing did descriptions [97,98], thus contributing to learning and memory not include observational coding of user behavior. As reported (eg, comprehension and retention of new content) [98,99]. Given in similar studies [30,87,88], this could have provided more the fact that motivational challenges are common among people concrete usability feedback and complement the results of the with SMI (eg, negative symptoms of schizophrenia), we built SUS and the thematic analysis of our transcripts. A third an app that had visual storytelling at its core. Our main goal methodological limitation is that we did not conduct diagnostic was to create an app with visuals and stories that were as interviews of our subjects. Despite this, all subjects received engaging as possible, in combination with gamification, psychiatric treatment and were recruited from a community well-established behavior analytic principles, and the use of mental health clinic, which, according to regulations by the US evidence-based smoking cessation content. Department of Health and Human Services , requires serving individuals with SMI status. As stated in the introduction, we believe that the determination of the active therapeutic ingredients delivered by an app should Third, the app’s tracking feature provided personalized feedback be the result of a careful design process. However, user-centered based on participants’ responses to self-reported ratings of mood design research could lead to stakeholder recommendations that and smoking behavior. However, this level of personalization are not consistent with evidence-based practices or theory-based did not take into consideration each individual’s baseline (eg, principles of change. Adherence to evidence-based practices or certain smoking reductions could be large or small depending theory-based principles of change might not always be on the individual’s baseline), limiting its impact for emphasized in user-centered design research, yet it is a key personalization. Likewise, the current app system does not take activity of the design process inherent in the original into account a variety of quitting scenarios (eg, individuals who user-centered design guidelines [31,32]. As shown in our quit before the end of the program) and how these scenarios http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 13 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al interact with app content. Future versions could take into alarmingly high rates of nicotine addiction and in high need of account these personal scenarios to strengthen Learn to Quit’s novel smoking cessation treatments. The feasibility and usefulness and level of personalization. acceptability of this app will be subsequently tested in a randomized controlled feasibility trial (clinicaltrials.gov Finally, this paper focuses on the user-centered design research NCT03069482). of a paper prototype leading to the development of the Learn to Quit app. Although this report does not provide data about User-centered design is a critical process for the development the usability and user experience of the final Learn to Quit app, of mobile interventions for individuals with SMI. However, it allowed us to transparently report in more detail its because emotional and cognitive challenges are present in less user-centered design process. A separate report of Learn to severe forms of mental illness or can be present in other health Quit’s usability and user experience in its target population is conditions (eg, cancer patients), the results of this study might under review elsewhere. be generalizable to other areas of mHealth research. Therefore, while ideating and designing digital interventions, mHealth Conclusions developers might consider capitalizing on the role of visual This is the first paper to systematically describe the rationale, engagement, storytelling, and the systematic application of ideation, design, and user research of a smoking cessation app behavior analytic principles to deliver evidence-based content. specifically designed for people with SMI, a population with Acknowledgments The authors thank Francis J McClernon, PhD, for comments and feedback that greatly improved the final manuscript. Roger Vilardaga received funding support from the National Institute of Drug Abuse (K99DA037276 and R00DA037276). Learn to Quit is the intellectual property of the University of Washington (© 2015-2016 University of Washington). Julie A. Kientz’s spouse is the cofounder of Senosis Health, a start-up company in the area of health technologies for diagnosis, monitoring, and treatment, which was recently acquired by Google. Authors' Contributions RV envisioned the app rationale and intervention, ideated app design and features, sketched imagery, conducted research activities, interpreted results, and wrote this manuscript. JR coordinated research activities, sketched imagery to be included in each module, and contributed to the overall conduct of design and research activities. EZ transcribed the recordings of the usability testing procedure, conducted thematic analysis of the interviews, discussed emerging themes with first author, and sketched imagery to be included in each module. JK provided user-centered design expertise in the development of the software app for behavior change and smoking cessation and contributed to early drafts of this manuscript. RR contributed to form the expert panel, provided serious mental illness expertise and feedback about the overall app design, and contributed to late drafts of this manuscript. CO created high-fidelity designs and illustrations and contributed to improvements in interaction design and the final user experience of the app. KH contributed to late drafts of this manuscript and to the layout of the manuscript structure and sections. Conflicts of Interest None declared. Multimedia Appendix 1 Primary and secondary personas that guided Phase IV of the user-centered design process. [PNG File, 197KB-Multimedia Appendix 1] Multimedia Appendix 2 Evidence-based smoking cessation content of Learn to Quit app. [PDF File (Adobe PDF File), 195KB-Multimedia Appendix 2] Multimedia Appendix 3 Design principles. [PDF File (Adobe PDF File), 242KB-Multimedia Appendix 3] References 1. Jamal A, King BA, Neff LJ, Whitmill J, Babb SD, Graffunder CM. Current Cigarette Smoking Among Adults -- United States, 2005-2015. MMWR Morb Mortal Wkly Rep 2016;65(44):1205-1211. [doi: 10.15585/mmwr.mm6544a2] http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 14 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al 2. Cook BL, Wayne GF, Kafali EN, Liu Z, Shu C, Flores M. Trends in smoking among adults with mental illness and association between mental health treatment and smoking cessation. J Am Med Assoc 2014 Jan 08;311(2):172-182 [FREE Full text] [doi: 10.1001/jama.2013.284985] [Medline: 24399556] 3. Substance Abuse and Mental Health Services Administration. 2016. Behind the Term: Serious Mental Illness URL: https:/ /nrepp.samhsa.gov/Docs/Literatures/Behind_the_Term_Serious%20%20Mental%20Illness.pdf[WebCite Cache ID 6wQoVKnDP] 4. Cook BL, Wayne GF, Kafali EN, Liu Z, Shu C, Flores M. Trends in smoking among adults with mental illness and association between mental health treatment and smoking cessation. J Am Med Assoc 2014 Jan 08;311(2):172-182 [FREE Full text] [doi: 10.1001/jama.2013.284985] [Medline: 24399556] 5. Dickerson F, Stallings CR, Origoni AE, Vaughan C, Khushalani S, Schroeder J, et al. Cigarette smoking among persons with schizophrenia or bipolar disorder in routine clinical settings, 1999-2011. Psychiatr Serv 2013 Jan;64(1):44-50. [doi: 10.1176/appi.ps.201200143] [Medline: 23280457] 6. Lasser K, Boyd JW, Woolhandler S, Himmelstein DU, McCormick D, Bor DH. Smoking and mental illness: a population-based prevalence study. J Am Med Assoc 2000;284(20):2606-2610. [Medline: 11086367] 7. Scott D, Happell B. The high prevalence of poor physical health and unhealthy lifestyle behaviours in individuals with severe mental illness. Issues Ment Health Nurs 2011;32(9):589-597. [doi: 10.3109/01612840.2011.569846] [Medline: 21859410] 8. Colton CW, Manderscheid RW. Congruencies in increased mortality rates, years of potential life lost, and causes of death among public mental health clients in eight states. Prev Chronic Dis 2006 Apr;3(2):A42 [FREE Full text] [Medline: 16539783] 9. Ben-Zeev D, Davis KE, Kaiser S, Krzsos I, Drake RE. Mobile technologies among people with serious mental illness: opportunities for future services. Adm Policy Ment Health 2013 Jul;40(4):340-343 [FREE Full text] [doi: 10.1007/s10488-012-0424-x] [Medline: 22648635] 10. Firth J, Cotter J, Torous J, Bucci S, Firth JA, Yung AR. Mobile phone ownership and endorsement of “mHealth” among people with psychosis: a meta-analysis of cross-sectional studies. Schizophr Bull 2015 Sep 22;42(2):448-455. [doi: 10.1093/schbul/sbv132] [Medline: 26400871] 11. Pew Research Center. Pewinternet. Mobile Fact Sheet Internet URL: http://www.pewinternet.org/fact-sheet/mobile/ [accessed 2017-09-01] [WebCite Cache ID 6t9qt6xMO] 12. Killikelly C, He Z, Reeder C, Wykes T. Improving adherence to web-based and mobile technologies for people with psychosis: systematic review of new potential predictors of adherence. JMIR Mhealth Uhealth 2017 Jul 20;5(7):e94 [FREE Full text] [doi: 10.2196/mhealth.7088] [Medline: 28729235] 13. Rotondi AJ, Sinkule J, Haas GL, Spring MB, Litschge CM, Newhill CE, et al. Designing websites for persons with cognitive deficits: design and usability of a psychoeducational intervention for persons with severe mental illness. Psychol Serv 2007 Aug;4(3):202-224 [FREE Full text] [doi: 10.1037/1541-15126.96.36.199] [Medline: 26321884] 14. Ben-Zeev D, Kaiser SM, Brenner CJ, Begale M, Duffecy J, Mohr DC. Development and usability testing of FOCUS: a smartphone system for self-management of schizophrenia. Psychiatr Rehabil J 2013 Dec;36(4):289-296 [FREE Full text] [doi: 10.1037/prj0000019] [Medline: 24015913] 15. Palmier-Claus J, Rogers A, Ainsworth J, Machin M, Barrowclough C, Laverty L, et al. Integrating mobile-phone based assessment for psychosis into people?s everyday lives and clinical care: a qualitative study. BMC Psychiatry 2013 Mar 2013 Jan 23;13(1):12. [doi: 10.1186/1471-244X-13-34] 16. Ferron JC, Brunette MF, McHugo GJ, Devitt TS, Martin WM, Drake RE. Developing a quit smoking website that is usable by people with severe mental illnesses. Psychiatr Rehabil J 2011;35(2):111-116. [doi: 10.2975/35.2.2011.111.116] [Medline: 22020840] 17. Granholm E, Ben-Zeev D, Link PC, Bradshaw KR, Holden JL. Mobile Assessment and Treatment for Schizophrenia (MATS): a pilot trial of an interactive text-messaging intervention for medication adherence, socialization, and auditory hallucinations. Schizophr Bull 2012 May;38(3):414-425 [FREE Full text] [doi: 10.1093/schbul/sbr155] [Medline: 22080492] 18. Bricker JB, Copeland W, Mull KE, Zeng EY, Watson NL, Akioka KJ, et al. Single-arm trial of the second version of an acceptance & commitment therapy smartphone application for smoking cessation. Drug Alcohol Depend 2017 Jan 01;170:37-42. [doi: 10.1016/j.drugalcdep.2016.10.029] [Medline: 27870987] 19. Bricker JB, Mull KE, Kientz JA, Vilardaga R, Mercer LD, Akioka KJ, et al. Randomized, controlled pilot trial of a smartphone app for smoking cessation using acceptance and commitment therapy. Drug Alcohol Depend 2014 Oct 1;143:87-94. [doi: 10.1016/j.drugalcdep.2014.07.006] [Medline: 25085225] 20. Buller DB, Borland R, Bettinghaus EP, Shane JH, Zimmerman DE. Randomized trial of a smartphone mobile application compared to text messaging to support smoking cessation. Telemed J E Health 2014 Mar;20(3):206-214 [FREE Full text] [doi: 10.1089/tmj.2013.0169] [Medline: 24350804] 21. Hafner K. NYtimes. Wanted by the Police: A Good Interface Internet URL: http://www.nytimes.com/2004/11/11/technology/ wanted-by-the-police-a-good-interface.html[WebCite Cache ID 6t9r2asCM] 22. Rutirasiri C. Entrepreneur. Making the Business Case for Human-Centered Design Internet URL: https://www. entrepreneur.com/article/239948 [accessed 2017-09-01] [WebCite Cache ID 6t9r4XgVm] http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 15 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al 23. Alvarez-Jimenez M, Alcazar-Corcoles MA, González-Blanch C, Bendall S, McGorry PD, Gleeson JF. Online, social media and mobile technologies for psychosis treatment: a systematic review on novel user-led interventions. Schizophr Res 2014 Jun;156(1):96-106. [doi: 10.1016/j.schres.2014.03.021] [Medline: 24746468] 24. Kessler RC, Berglund PA, Bruce ML, Koch JR, Laska EM, Leaf PJ, et al. The prevalence and correlates of untreated serious mental illness. Health Serv Res 2001 Dec;36(6 Pt 1):987-1007 [FREE Full text] [Medline: 11775672] 25. Kessler R, Chiu W, Demler O, Walters E. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005 Jun 01;62(6):617. [doi: 10.1001/archpsyc.62.6.617] [Medline: PMC2847357] 26. Green MF, Kern RS, Heaton RK. Longitudinal studies of cognition and functional outcome in schizophrenia: implications for MATRICS. Schizophr Res 2004 Dec 15;72(1):41-51. [doi: 10.1016/j.schres.2004.09.009] [Medline: 15531406] 27. Austin MP, Mitchell P, Goodwin GM. Cognitive deficits in depression: possible implications for functional neuropathology. Br J Psychiatry 2001 Mar;178:200-206 [FREE Full text] [Medline: 11230029] 28. Schwartz BL, Rosse RB, Veazey C, Deutsch SI. Impaired motor skill learning in schizophrenia: implications for corticostriatal dysfunction. Biol Psychiatry 1996 Feb;39(4):241-248. [doi: 10.1016/0006-3223(95)00130-1] 29. Zeng EY, Vilardaga R, Heffner JL, Mull KE, Bricker JB. Predictors of utilization of a novel smoking cessation smartphone app. Telemed J E Health 2015 Jul 14;21(12):998-1004. [doi: 10.1089/tmj.2014.0232] [Medline: 26171733] 30. Vilardaga R, Rizo J, Kientz JA, McDonell MG, Ries RK, Sobel K. User experience evaluation of a smoking cessation app in people with serious mental illness. Nicotine Tob Res 2016 May;18(5):1032-1038. [doi: 10.1093/ntr/ntv256] [Medline: 26581430] 31. ISO. Ergonomics of human-system interaction -- Part 210: Human-centred design for interactive systems URL: https:/ /www.iso.org/standard/52075.html [accessed 2017-11-08] [WebCite Cache ID 6uyKOKinG] 32. International Organization for Standardization. ISO 13407Human-centred design processes for interactive systems Internet URL: https://www.iso.org/standard/21197.html [accessed 2017-11-14] [WebCite Cache ID 6uyJdsDdC] 33. Hartson R, Pyla P. The UX Book: Process and Guidelines for Ensuring a Quality User Experience. 1 edition. Amsterdam, Boston: Morgan Kaufmann; 2012. 34. Bhattacharya A, Vilardaga R, Kientz JA, Munson SA. Lessons from Practice: Designing Tools to Facilitate Individualized Support for Quitting Smoking. USA: ACM; 2017 Presented at: ACM Trans Comput Hum Interact; 5/6/2017; Denver p. 3057-3070 URL: http://europepmc.org/abstract/MED/29123362 [doi: 10.1145/3025453.3025725] 35. McDonell M, Howell D, McPherson S, Cameron J, Srebnik D, Roll J, et al. Voucher-based reinforcement for alcohol abstinence using the ethyl-glucuronide alcohol biomarker. J Appl Behav Anal 2012;45(1):161-165 [FREE Full text] [doi: 10.1901/jaba.2012.45-161] [Medline: 22403460] 36. McDonell MG, Leickly E, McPherson S, Skalisky J, Srebnik D, Angelo F, et al. A randomized controlled trial of ethyl glucuronide-based contingency management for outpatients with co-occurring alcohol use disorders and serious mental illness. Am J Psychiatry 2017 Apr 01;174(4):370-377. [doi: 10.1176/appi.ajp.2016.16050627] [Medline: 28135843] 37. Dallery J, Kurti A, Erb P. A new frontier: integrating behavioral and digital technology to promote health behavior. Behav Anal 2015 May;38(1):19-49 [FREE Full text] [doi: 10.1007/s40614-014-0017-y] [Medline: 27347477] 38. Rotondi AJ, Sinkule J, Haas GL, Spring MB, Litschge CM, Newhill CE, et al. Designing websites for persons with cognitive deficits: design and usability of a psychoeducational intervention for persons with severe mental illness. Psychol Serv 2007 Aug;4(3):202-224 [FREE Full text] [doi: 10.1037/1541-15188.8.131.52] [Medline: 26321884] 39. Stephanidis C. User Interfaces for All: Concepts, Methods,Tools. Hillsdale, NJ, USA: CRC Press; 2000. 40. Norman D, Berkrot P. The Design of Everyday Things. Old Saybrook: Tantor Audio; 2011. 41. Dalal NP, Quible Z, Wyatt K. Cognitive design of home pages: an experimental study of comprehension on the World Wide Web. Inf Process Manag 2000 Jul;36(4):607-621. [doi: 10.1016/S0306-4573(99)00071-0] 42. Cooper A, Reimann R, Cronin D. About Face 3: The Essentials of Interaction Design. 3rd edition. Indianapolis, IN: Wiley; 43. LeRouge C, Ma J, Sneha S, Tolle K. User profiles and personas in the design and development of consumer health technologies. Int J Med Inform 2013 Nov;82(11):e251-e268. [doi: 10.1016/j.ijmedinf.2011.03.006] [Medline: 21481635] 44. Miaskiewicz T, Kozar KA. Personas and user-centered design: how can personas benefit product design processes? Design Studies 2011 Sep;32(5):417-430. [doi: 10.1016/j.destud.2011.03.003] 45. Buxton B. Sketching User Experiences: Getting the Design Right and the Right Design. 1 edition. San Francisco, Calif: Morgan Kaufmann; 2007. 46. Sauro J. A practical guide to the system usability scale: Background, benchmarks & best practices. Denver: Measuring Usability LLC; 2011. 47. Rubin J, Chisnell D, Spool J. Handbook of Usability Testing: How to Plan, Design, and Conduct Effective Tests. 2 edition. Indianapolis, IN: Wiley; 2008. 48. Lewis JR, Sauro J. The Factor Structure of the System Usability Scale. In: Human Centered Design. Berlin, Heidelberg: Springer; 2009. 49. Rogers Y, Sharp H, Preece J. Interaction Design: Beyond Human - Computer Interaction. West Sussex: John Wiley & Sons; 2011. http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 16 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al 50. IDEO. Human-Centered Design Toolkit: An Open-Source Toolkit To Inspire New Solutions in the Developing World. 2 edition. S. l. Canada: IDEO; 2011. 51. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol 2006 Jan;3(2):77-101. [doi: 10.1191/1478088706qp063oa] 52. Khwaja A, Silverman D, Sloan F. Citeseerx.ist. 2006. Time Preference, Time Discounting, and Smoking Decisions Internet URL: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.614.1395&rep=rep1&type=pdf 53. Smashing Ideas. URL: http://smashingideas.com/ [accessed 2017-09-01] [WebCite Cache ID 6t9rKtsWX] 54. Hayes S, Strosahl K, Wilson K. Acceptance and Commitment Therapy: The Process and Practice of Mindful Change. Second Edition: Guilford Press; 2011. 55. Bricker JB, Mann SL, Marek PM, Liu J, Peterson AV. Telephone-delivered acceptance and commitment therapy for adult smoking cessation: a feasibility study. Nicotine Tob Res 2010 Apr;12(4):454-458. [doi: 10.1093/ntr/ntq002] [Medline: 20142417] 56. Gifford EV, Kohlenberg BS, Hayes SC, Pierson HM, Piasecki MP, Antonuccio DO, et al. Does acceptance and relationship focused behavior therapy contribute to bupropion outcomes? A randomized controlled trial of functional analytic psychotherapy and acceptance and commitment therapy for smoking cessation. Behav Ther 2011 Dec;42(4):700-715. [doi: 10.1016/j.beth.2011.03.002] [Medline: 22035998] 57. Hernández-López M, Luciano MC, Bricker JB, Roales-Nieto JG, Montesinos F. Acceptance and commitment therapy for smoking cessation: a preliminary study of its effectiveness in comparison with cognitive behavioral therapy. Psychol Addict Behav 2009 Dec;23(4):723-730. [doi: 10.1037/a0017632] [Medline: 20025380] 58. Bricker J, Wyszynski C, Comstock B, Heffner JL. Pilot randomized controlled trial of web-based acceptance and commitment therapy for smoking cessation. Nicotine Tob Res 2013 Oct;15(10):1756-1764 [FREE Full text] [doi: 10.1093/ntr/ntt056] [Medline: 23703730] 59. Vilardaga R, Heffner JL, Mercer LD, Bricker JB. Do counselor techniques predict quitting during smoking cessation treatment? A component analysis of telephone-delivered Acceptance and Commitment Therapy. Behav Res Ther 2014 Oct;61:89-95 [FREE Full text] [doi: 10.1016/j.brat.2014.07.008] [Medline: 25156397] 60. Heffner JL, Vilardaga R, Mercer LD, Kientz JA, Bricker JB. Feature-level analysis of a novel smartphone application for smoking cessation. Am J Drug Alcohol Abuse 2015 Jan;41(1):68-73. [doi: 10.3109/00952990.2014.977486] [Medline: 25397860] 61. Bricker JB, Schiff L, Comstock BA. Does avoidant coping influence young adults' smoking?: a ten-year longitudinal study. Nicotine Tob Res 2011 Oct;13(10):998-1002 [FREE Full text] [doi: 10.1093/ntr/ntr074] [Medline: 21543547] 62. Gaudiano BA, Herbert JD. Acute treatment of inpatients with psychotic symptoms using Acceptance and Commitment Therapy: pilot results. Behav Res Ther 2006 Mar;44(3):415-437. [doi: 10.1016/j.brat.2005.02.007] [Medline: 15893293] 63. Gaudiano BA, Nowlan K, Brown LA, Epstein-Lubow G, Miller IW. An open trial of a new acceptance-based behavioral treatment for major depression with psychotic features. Behav Modif 2013 May;37(3):324-355 [FREE Full text] [doi: 10.1177/0145445512465173] [Medline: 23223385] 64. White R, Gumley A, McTaggart J, Rattrie L, McConville D, Cleare S, et al. A feasibility study of Acceptance and Commitment Therapy for emotional dysfunction following psychosis. Behav Res Ther 2011 Dec;49(12):901-907. [doi: 10.1016/j.brat.2011.09.003] [Medline: 21975193] 65. Bach P, Hayes SC. The use of acceptance and commitment therapy to prevent the rehospitalization of psychotic patients: A randomized controlled trial. J Consult Clin Psychol 2002;70(5):1129-1139. [doi: 10.1037//0022-006X.70.5.1129] 66. 2008 PHS Guideline Update Panel, Liaisons, and Staff. Treating tobacco use and dependence: 2008 update U.S. Public Health Service Clinical Practice Guideline executive summary. Respir Care 2008 Sep;53(9):1217-1222 [FREE Full text] [Medline: 18807274] 67. El-Hilly AA, Iqbal SS, Ahmed M, Sherwani Y, Muntasir M, Siddiqui S, et al. Game on? Smoking cessation through the gamification of mHealth: a longitudinal qualitative study. JMIR Serious Games 2016 Oct 24;4(2):e18 [FREE Full text] [doi: 10.2196/games.5678] [Medline: 27777216] 68. Choi J, Bakken S. Web-based education for low-literate parents in Neonatal Intensive Care Unit: Development of a website and heuristic evaluation and usability testing. Int J Med Inform 2010 May 9;79(8):565-575. [doi: 10.1016/j.ijmedinf.2010.05.001] 69. Miller AS, Cafazzo JA, Seto E. A game plan: Gamification design principles in mHealth applications for chronic disease management. Health Informatics J 2014 Jul 1;22(2):184-193. [doi: 10.1177/1460458214537511] [Medline: 24986104] 70. Donkin L, Christensen H, Naismith SL, Neal B, Hickie IB, Glozier N. A systematic review of the impact of adherence on the effectiveness of e-therapies. J Med Internet Res 2011 Aug;13(3):e52 [FREE Full text] [doi: 10.2196/jmir.1772] [Medline: 21821503] 71. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005 Jun;62(6):593-602. [doi: 10.1001/archpsyc.62.6.593] [Medline: 15939837] 72. Catania A, Brigham T. Handbook of Applied Behavior Analysis: social and Instructional Processes. Michigan: Irvington Publishers; 1978. http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 17 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al 73. Cooper J, Heron T, Heward W. Applied Behavior Analysis. 2 edition. Upper Saddle River, NJ: Pearson; 2007:978. 74. Glynn T. Antecedent control of behaviour in educational contexts. Educ Psychol (Lond) 2010 Nov 25;2(3-4):215-229. [doi: 10.1080/0144341820020305] 75. Blohm I, Leimeister JM. Gamification: Design of IT-Based Enhancing Services for Motivational Support and Behavioral Change. Bus Inf Syst Eng 2013 Jun 14;5(4):275-278. [doi: 10.1007/s12599-013-0273-5] 76. Morford ZH, Witts BN, Killingsworth KJ, Alavosius MP. Gamification: the intersection between behavior analysis and game design technologies. Behav Anal 2014 May;37(1):25-40 [FREE Full text] [doi: 10.1007/s40614-014-0006-1] [Medline: 27274957] 77. Koegel RL, Shirotova L, Koegel LK. Antecedent stimulus control: using orienting cues to facilitate first-word acquisition for nonresponders with autism. Behav Anal 2009;32(2):281-284 [FREE Full text] [Medline: 22478527] 78. Ross DE, Greer RD. Generalized imitation and the mand: inducing first instances of speech in young children with autism. Res Dev Disabil 2003;24(1):58-74. [Medline: 12553968] 79. Tsiouri I, Schoen SE, Paul R. Enhancing the application and evaluation of a discrete trial intervention package for eliciting first words in preverbal preschoolers with ASD. J Autism Dev Disord 2012 Jul;42(7):1281-1293. [doi: 10.1007/s10803-011-1358-y] [Medline: 21918912] 80. Engberg S, Sereika SM, McDowell BJ, Weber E, Brodak I. Effectiveness of prompted voiding in treating urinary incontinence in cognitively impaired homebound older adults. J Wound Ostomy Continence Nurs 2002 Sep;29(5):252-265. [Medline: 12510471] 81. Adkins VK, Mathews RM. Prompted voiding to reduce incontinence in community-dwelling older adults. J Appl Behav Anal 1997;30(1):153-156 [FREE Full text] [doi: 10.1901/jaba.1997.30-153] [Medline: 9103990] 82. Feliciano L, Vore J, LeBlanc LA, Baker JC. Decreasing entry into a restricted area using a visual barrier. J Appl Behav Anal 2004;37(1):107-110 [FREE Full text] [doi: 10.1901/jaba.2004.37-107] [Medline: 15154224] 83. Zencius AH, Wesolowski MD, Burke WH, McQuade P. Antecedent control in the treatment of brain-injured clients. Brain Injury 2009 Jul 03;3(2):199-205. [doi: 10.3109/02699058909004553] 84. Ylvisaker M, Turkstra LS, Coelho C. Behavioral and social interventions for individuals with traumatic brain injury: a summary of the research with clinical implications. Semin Speech Lang 2005 Nov;26(4):256-267. [doi: 10.1055/s-2005-922104] [Medline: 16278797] 85. Edwards J. Business Insider. These Maps Show That Android Is For Poor People Internet URL: http://www. businessinsider.com/android-is-for-poor-people-maps-2014-4 [accessed 2017-09-01] [WebCite Cache ID 6t9raSJIV] 86. Shaffer D. WebpageFX. iPhones Dominate Smartphone Market Share for Internet Usage Internet URL: https://www. webpagefx.com/blog/general/iphone-smartphone-market-share/ [accessed 2017-09-01] [WebCite Cache ID 6t9rgXION] 87. Stiles-Shields C, Montague E, Lattie EG, Schueller SM, Kwasny MJ, Mohr DC. Exploring user learnability and learning performance in an app for depression: usability study. JMIR Hum Factors 2017 Aug 11;4(3):e18 [FREE Full text] [doi: 10.2196/humanfactors.7951] [Medline: 28801301] 88. Sarkar U, Gourley GI, Lyles CR, Tieu L, Clarity C, Newmark L, et al. Usability of commercially available mobile applications for diverse patients. J Gen Intern Med 2016 Dec;31(12):1417-1426. [doi: 10.1007/s11606-016-3771-6] [Medline: 27418347] 89. Roepke AM, Jaffee SR, Riffle OM, McGonigal J, Broome R, Maxwell B. Randomized controlled trial of SuperBetter, a smartphone-based/internet-based self-help tool to reduce depressive symptoms. Games Health J 2015 Jun;4(3):235-246. [doi: 10.1089/g4h.2014.0046] [Medline: 26182069] 90. Rabbi M, Ali S, Choudhury T, Berke E. Passive and in-situ assessment of mental and physical well-being using mobile sensors. Proc ACM Int Conf Ubiquitous Comput 2011;2011:385-394 [FREE Full text] [doi: 10.1145/2030112.2030164] [Medline: 25285324] 91. Berke EM, Choudhury T, Ali S, Rabbi M. Objective measurement of sociability and activity: mobile sensing in the community. Ann Fam Med 2011;9(4):344-350 [FREE Full text] [doi: 10.1370/afm.1266] [Medline: 21747106] 92. Wang R, Aung M, Abdullah S, Brian R, Campbell A, Choudhury T, et al. CrossCheck: Toward Passive Sensing Detection of Mental Health Changes in People with Schizophrenia. USA: ACM; 2016 Presented at: Proceedings of the 2016 ACM International Joint Conference on Pervasive Ubiquitous Computing; 9/12/2016; Heidelberg. 93. Rahman T, Adams A, Ravichandran R, Zhang M, Patel S, Kientz J, et al. DoppleSleep: A Contactless Unobtrusive Sleep Sensing System Using Short-range Doppler Radar. USA: ACM; 2015 Presented at: Proceedings of the 2015 ACM International Joint Conference on Pervasive Ubiquitous Computing Internet; 7/11/2015; Osaka. 94. Consolvo S, McDonald D, Toscos T, Chen M, Froehlich J, Harrison B, et al. Activity Sensing in the Wild: A Field Trial of Ubifit Garden. USA: ACM; 2008 Presented at: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems Internet; 4/5/2008; Florence, Italy. 95. Bruner J. Making Stories: Law, Literature, Life. Cambridge: Harvard University Press; 2003. 96. Barraza JA, Zak PJ. Empathy toward strangers triggers oxytocin release and subsequent generosity. Ann N Y Acad Sci 2009 Jun;1167:182-189. [doi: 10.1111/j.1749-6632.2009.04504.x] [Medline: 19580564] 97. Grabenhorst F, Rolls ET. Value, pleasure and choice in the ventral prefrontal cortex. Trends Cogn Sci 2011 Feb;15(2):56-67. [doi: 10.1016/j.tics.2010.12.004] [Medline: 21216655] http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 18 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Vilardaga et al 98. Conway BR, Rehding A. Neuroaesthetics and the trouble with beauty. PLoS Biol 2013 Mar;11(3):e1001504 [FREE Full text] [doi: 10.1371/journal.pbio.1001504] [Medline: 23526878] 99. Ishai A, Fairhall SL, Pepperell R. Perception, memory and aesthetics of indeterminate art. Brain Res Bull 2007 Jul 12;73(4-6):319-324. [doi: 10.1016/j.brainresbull.2007.04.009] [Medline: 17562398] 100. The Center for Universal Design. URL: https://projects.ncsu.edu/ncsu/design/cud/about_ud/udprinciples.htm[WebCite Cache ID 6t9rrHeDw] 101. Nielsen J, Landauer T. A Mathematical Model of the Finding of Usability Problems. USA: ACM; 1993 Presented at: Proceedings of the INTERACTION and CHI Conference on Human Factors in Computing Systems Internet; 1993; New York, NY. 102. Faulkner L. Beyond the five-user assumption: benefits of increased sample sizes in usability testing. Behav Res Methods Instrum Comput 2003 Aug;35(3):379-383. [doi: 10.3758/BF03195514] Abbreviations ACT: Acceptance and Commitment Therapy NCI: National Cancer Institute SMI: serious mental illness SUS: system usability scale USCPG: US Clinical Practice Guidelines Edited by A Powell; submitted 03.09.17; peer-reviewed by M Levin, S Schueller, K Bold, C Bullen; comments to author 06.10.17; revised version received 10.11.17; accepted 25.11.17; published 16.01.18 Please cite as: Vilardaga R, Rizo J, Zeng E, Kientz JA, Ries R, Otis C, Hernandez K JMIR Serious Games 2018;6(1):e2 URL: http://games.jmir.org/2018/1/e2/ doi: 10.2196/games.8881 PMID: 29339346 ©Roger Vilardaga, Javier Rizo, Emily Zeng, Julie A Kientz, Richard Ries, Chad Otis, Kayla Hernandez. Originally published in JMIR Serious Games (http://games.jmir.org), 16.01.2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Serious Games, is properly cited. The complete bibliographic information, a link to the original publication on http://games.jmir.org, as well as this copyright and license information must be included. http://games.jmir.org/2018/1/e2/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e2 | p. 19 (page number not for citation purposes) XSL FO RenderX
JMIR Serious Games – JMIR Publications
Published: Jan 16, 2018
Keywords: smoking cessation; mHealth; serious mental illness; user-centered design; gamification; acceptance and commitment therapy
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